Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Embarrassingly Parallel Computations Chapter 3

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Embarrassingly Parallel Computations A computation that can obviously be divided into a number of completely independent parts, each of which can be executed by a separate process. No communication or very little communication between processes Each process can do its tasks without any interaction with others

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Practical embarrassingly parallel computation (static process creation / master-slave approach)

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Embarrassingly Parallel Computations Examples Low level image processing Many of such operations only involve local data with very limited if any communication between areas of interest. Mandelbrot set Monte Carlo Calculations

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Some geometrical operations Shifting Object shifted by Dx in the x-dimension and Dy in the y-dimension: x¢ = x + Dx y¢ = y + Dy where x and y are the original and x¢ and y¢ are the new coordinates. Scaling Object scaled by a factor S x in x-direction and S y in y-direction: x¢ = xS x y¢ = yS y Rotation Object rotated through an angle q about the origin of the coordinate system: x¢ = x cosq + y sinq y¢ = -x sinq + y cosq

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Partitioning into regions for individual processes Square region for each process (can also use strips)

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Mandelbrot Set What is the Mandelbrot set? Set of all complex numbers c for which sequence defined by the following iteration remains bounded: z(0) = c, z(n+1) = z(n)*z(n) + c, n=0,1,2,... This means that there is a number B such that the absolute value of all iterates z(n) never gets larger than B.

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Sequential routine computing value of one point structure complex { float real; float imag; }; int cal_pixel(complex c) {int count, max; complex z; float temp, lengthsq; max = 256; z.real = 0; z.imag = 0; count = 0; /* number of iterations */ do { temp = z.real * z.real - z.imag * z.imag + c.real; z.imag = 2 * z.real * z.imag + c.imag; z.real = temp; lengthsq = z.real * z.real + z.imag * z.imag; count++; } while ((lengthsq < 4.0) && (count < max)); return count; }

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Parallelizing Mandelbrot Set Computation Static Task Assignment Simply divide the region into fixed number of parts, each computed by a separate processor. Disadvantage: Different regions may require different numbers of iterations and time. Dynamic Task Assignment

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Monte Carlo Methods Another embarrassingly parallel computation Example: calculate π using the ratio: Randomly choose points within the square Count the points that lie within the circle Given a sufficient number of randomly selected samples fraction of points within the circle will be: π /4

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Example of Monte Carlo Method Area = D 2

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Use random values of x to compute f(x) and sum values of f(x): where x i are randomly generated values of x between x 1 and x 2. Monte Carlo method is very useful if the function cannot be integrated numerically (maybe having a large number of variables) Computing an Integral

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Example: Sequential Code sum = 0; for (i = 0; i < N; i++) { /* N random samples */ xr = rand_v(x1, x2); /* generate next random value */ sum = sum + xr * xr - 3 * xr; /* compute f(xr) */ } area = (sum / N) * (x2 - x1); Routine rand_v(x1, x2) returns a pseudorandom number between x1 and x2

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. Values for a "good" generator: a=16807, m= (a prime number), c=0 This generates a repeating sequence of ( ) different numbers.

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved. A Parallel Formulation A and C above can be derived by computing x i+1 =f(x i ), x i+2 =f(f(x i )),... x i+k =f(f(f(…f(x i )))) and using the following properties: (A+B) mod M = [(A mod M) + (B mod M)] mod M [ X(A mod M) ] mod M = (X.A mod M) X(A + B) mod M = (X.A + X.B) mod M = [(X.A mod M) + (X.B mod M)] mod M [ X( (A+B) mod M) ] mod M = (X.A + X.B) mod M x i+1 = (ax i + c) mod m x i+k = (Ax i + C) mod m

Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M Pearson Education Inc. All rights reserved.